code: def crop_by_boxes(
      input_tensor: Tensor,
      src_box: Tensor,
      dst_box: Tensor,
      mode: str = "bilinear",
      padding_mode: str = "zeros",
      align_corners: bool = True,
      validate_boxes: bool = True,
  ) -> Tensor:
      """Perform crop transform on 2D images (4D tensor) given two bounding boxes.
  
      Given an input tensor, this function selected the interested areas by the provided bounding boxes (src_box).
      Then the selected areas would be fitted into the targeted bounding boxes (dst_box) by a perspective transformation.
      So far, the ragged tensor is not supported by PyTorch right now. This function hereby requires the bounding boxes
      in a batch must be rectangles with same width and height.
  
      Args:
          input_tensor: the 2D image tensor with shape (B, C, H, W).
          src_box: a tensor with shape (B, 4, 2) containing the coordinates of the bounding boxes
              to be extracted. The tensor must have the shape of Bx4x2, where each box is defined in the clockwise
              order: top-left, top-right, bottom-right and bottom-left. The coordinates must be in x, y order.
          dst_box: a tensor with shape (B, 4, 2) containing the coordinates of the bounding boxes
              to be placed. The tensor must have the shape of Bx4x2, where each box is defined in the clockwise
              order: top-left, top-right, bottom-right and bottom-left. The coordinates must be in x, y order.
          mode: interpolation mode to calculate output values
            ``'bilinear'`` | ``'nearest'``.
          padding_mode: padding mode for outside grid values
            ``'zeros'`` | ``'border'`` | ``'reflection'``.
          align_corners: mode for grid_generation.
          validate_boxes: flag to perform validation on boxes.
  
      Returns:
          Tensor: the output tensor with patches.
  
      Examples:
          >>> input = torch.arange(16, dtype=torch.float32).reshape((1, 1, 4, 4))
          >>> src_box = torch.tensor([[
          ...     [1., 1.],
          ...     [2., 1.],
          ...     [2., 2.],
          ...     [1., 2.],
          ... ]])  # 1x4x2
          >>> dst_box = torch.tensor([[
          ...     [0., 0.],
          ...     [1., 0.],
          ...     [1., 1.],
          ...     [0., 1.],
          ... ]])  # 1x4x2
          >>> crop_by_boxes(input, src_box, dst_box, align_corners=True)
          tensor([[[[ 5.0000,  6.0000],
                    [ 9.0000, 10.0000]]]])
  
      Note:
          If the src_box is smaller than dst_box, the following error will be thrown.
          RuntimeError: solve_cpu: For batch 0: U(2,2) is zero, singular U.
      """
      if validate_boxes:
          validate_bbox(src_box)
          validate_bbox(dst_box)
  
      if len(input_tensor.shape) != 4:
          raise AssertionError(
              f"Only tensor with shape (B, C, H, W) supported. Got {input_tensor.shape}."
          )
  
      # compute transformation between points and warp
      # Note: Tensor.dtype must be float. "solve_cpu" not implemented for 'Long'
      dst_trans_src: Tensor = get_perspective_transform(
          src_box.to(input_tensor), dst_box.to(input_tensor)
      )
  
      bbox: Tuple[Tensor, Tensor] = infer_bbox_shape(dst_box)
      if not (
          (bbox[0] == bbox[0][0]).all()
          and (bbox[1] == bbox[1][0]).all()
      ):
          raise AssertionError(
              f"Cropping height, width and depth must be exact same in a batch. Got height {bbox[0]} and width {bbox[1]}."
          )
  
      h_out: int = int(bbox[0][0].item())
      w_out: int = int(bbox[1][0].item())
  
      return crop_by_transform_mat(
          input_tensor,
          dst_trans_src,
          (h_out, w_out),
          mode=mode,
          padding_mode=padding_mode,
          align_corners=align_corners,
      )
  
file_path: kornia/kornia/geometry/transform/crop2d.py
imports: from typing import Optional, Tuple
  import torch
  from kornia.core import Tensor, as_tensor, pad, tensor
  from kornia.geometry.bbox import infer_bbox_shape, validate_bbox
  from .affwarp import resize
  from .imgwarp import get_perspective_transform, warp_affine
label_desc: Use image node_id 3 for calling a global function with example usage: crop_by_boxes(input_tensor, src_box, dst_box, mode, padding_mode, align_corners, validate_boxes) and returns: crop_by_transform_mat